Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models

• 成果类型: Article
• 署名作者: Judd, Kenneth L.; Maliar, Lilia; Maliar, Serguei
• 署名单位: Stanford University; National Bureau of Economic Research; Universitat d'Alacant
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE14
• 发表日期: 2011
• 页码: 173-210
• 关键词: Stochastic simulation; generalized stochastic simulation algorithm; parameterized expectations algorithm; least absolute deviations; linear programming regularization

摘要

We develop numerically stable and accurate stochastic simulation approaches for solving dynamic economic models. First, instead of standard least-squares approximation methods, we examine a variety of alternatives, including least-squares methods using singular value decomposition and Tikhonov regularization, least-absolute deviations methods, and principal component regression method, all of which are numerically stable and can handle ill-conditioned problems. Second, instead of conventional Monte Carlo integration, we use accurate quadrature and monomial integration. We test our generalized stochastic simulation algorithm (GSSA) in three applications: the standard representative-agent neoclassical growth model, a model with rare disasters, and a multicountry model with hundreds of state variables. GSSA is simple to program, and MATLAB codes are provided.